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library(qiime2R)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
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  print.htmlwidget tools:rstudio
library(tidyr)
library(microbiome)
Loading required package: phyloseq
Loading required package: ggplot2

microbiome R package (microbiome.github.com)
    


 Copyright (C) 2011-2019 Leo Lahti, 
    Sudarshan Shetty et al. <microbiome.github.io>


Attaching package: ‘microbiome’

The following object is masked from ‘package:ggplot2’:

    alpha

The following object is masked from ‘package:base’:

    transform
library(compositions)
Welcome to compositions, a package for compositional data analysis.
Find an intro with "? compositions"


Attaching package: ‘compositions’

The following objects are masked from ‘package:stats’:

    anova, cor, cov, dist, var

The following objects are masked from ‘package:base’:

    %*%, norm, scale, scale.default
library(Dict)
library(reshape2)

Attaching package: ‘reshape2’

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    smiths
library(heatmaply)
Loading required package: plotly

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

Loading required package: viridis
Loading required package: viridisLite
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  method     from 
  rev.hclust vegan
Registered S3 method overwritten by 'seriation':
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  reorder.hclust vegan

======================
Welcome to heatmaply version 1.3.0

Type citation('heatmaply') for how to cite the package.
Type ?heatmaply for the main documentation.

The github page is: https://github.com/talgalili/heatmaply/
Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
You may ask questions at stackoverflow, use the r and heatmaply tags: 
     https://stackoverflow.com/questions/tagged/heatmaply
======================


Attaching package: ‘heatmaply’

The following object is masked from ‘package:compositions’:

    normalize
library(KEGGREST)

#This code block is to stage all the files needed for visualization


#Stage taxonomy file using Qiime2R
taxa_vsearch <- parse_taxonomy(taxonomy = read_qza('../taxonomy_vsearch/classification.qza')$data)
taxa_blast <- parse_taxonomy(taxonomy = read_qza('../taxonomy_blast/classification-blast.qza')$data)

#Stage metadata file
sample_metadata <- read.csv('../read-files/metadata.tsv', sep='\t')

#Stage unstratified/strat kegg pathway files
unstrat_kegg <- read.csv('../picrust2-strat/out/KEGG_pathways_out/path_abun_unstrat.tsv', sep = '\t')
strat_kegg <- read.csv('../picrust2-strat/out/KEGG_pathways_out/path_abun_strat.tsv', sep = '\t')

#Stage unstrat/strat KEGG ortholog files 
ko_unstrat <- read.csv('../picrust2-strat/out/KO_metagenome_out/unstrat_ko_metagenome.tsv', sep = '\t')
ko_strat <- read.csv('../picrust2-strat/out/KO_metagenome_out/strat_ko_metagenome.tsv', sep = '\t')

#Open selected KOs (for plotting relevant Kegg pathways)
deg_kos <- read.csv('./select_kos.txt', sep = '\t', header = TRUE)
rownames(deg_kos) <- deg_kos[,1]
deg_kos[,1] <- NULL

#open the pathway to ko file
pth_2_ko <- read.csv('~/bioinfo_pipelines/picrust2-2.4.2/picrust2/default_files/pathway_mapfiles/KEGG_pathways_to_KO.tsv', header =FALSE, sep='\t')
#Filter to only Select KOs

pth_2_ko <- pth_2_ko[pth_2_ko$V1 %in% rownames(deg_kos),]

#Retrieve Sample ids so I can call it as a variable 
sample_ids <- c(as.character(sample_metadata$Sample.id))

#retrieve BRITE hierarchy from KEGG using KEGGREST

keggGet(dbentries = 'br08901')
Error in .getUrl(url, .flatFileParser) : Not Found (HTTP 404).

#This code block is to load the required files to plot NSTI vs relative abundance
abs_count_otu <-  read_qza(file = '../dada2/table.qza')$data
rel_count_otu <- as.data.frame(apply(abs_count_otu,2,function(x){x/sum(x)}))
nsti_asvs <- as.data.frame(read.csv('../picrust2-strat/out/marker_predicted_and_nsti.tsv.gz', sep='\t', row.names = 1))


rel_nsti <- merge(nsti_asvs,rel_count_otu,by="row.names",all.x=TRUE)


rel_nsti <- melt(rel_nsti, measure.vars = sample_ids, variable.name = 'sample.id', value.name = 'rel.abundance')


#Group accding to consortia



ggplot_rel_plot <- ggplot(data=rel_nsti,mapping= aes(x=metadata_NSTI, y=rel.abundance, color=sample.id)) + geom_point()

plot(ggplot_rel_plot)

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